Meta-cognitive interval type-2 fuzzy controller for quadcopter flight control- an EEG based approach

This paper presents a method for a hands-off noninvasive brain computer interface (BCI) to control the movement of a quadcopter. The quadcopter model used in this paper is the AR.drone 2.0 and the non-invasive BCI device used is the Emotiv EPOC. A framework is developed to convert the raw EEG signals into commands to control the flight of quadcopter, AR.drone 2.0 through a wireless interface. The common spatial pattern algorithm is used to extract features from the EEG signals. The signals are classified using the meta-cognitive interval type-2 neuro-fuzzy inference system. The decoded intentions of the user, commands (move left, move right) are relayed to the quad over a wireless connection. Experiments show that the user is able to fly the quadcopter successfully without using hands.